People these days have a seemingly endless number of options when it comes to interacting with the digital world. There is a virtually infinite number of digital media objects and activities at their fingertips such as videos, music, games, websites, and virtual worlds. In addition, the advances in computing make it possible to personalize digital content for users, not only by bringing users specific content of their liking (such as streaming music, videos, loading games, or virtual worlds), but also generating content especially tailored to their taste (e.g., by rendering specific images in videos or games). Such personalization makes it possible for each user to get the most suitable, enjoyable and effective content on demand. However, in order to optimize and tune an experience to an individual user's liking, it is important to be able to discern the user's specific reaction to various objects and/or changes in specific details.
One of the main problems limiting widespread adoption of advanced personalization of digital experiences is the inadequacy of current user preference modeling. While models of a user's implicit preferences can be created by analysis of the user's digital footprint (e.g., visit to websites, online purchases, or semantic analysis of user correspondences), they can typically only answer broad questions regarding the user's preferences toward the entire content or experience. For example, such models are able to provide answers to broad questions like: Does the user like action videos or romantic comedies? Does the user like cars? Similarly, analysis of the user's explicit preference indications (e.g., Facebook's “Like” or Google's “+1” buttons) only provides information on the user's feeling towards content items in their entirety (e.g., a website, a video clip, or a book purchased on Amazon).
One area in computer science, which has been showing tremendous progress in recent years, is affective computing. Advances in the area of affective computing are making it possible to continuously monitor a user's emotional state (also called affective response), using a wide array of sensors that measure physiological signals and/or behavioral queues. As the technology advances and the systems used to measure affective response are becoming cheaper, much smaller and more comfortable to wear, affective computing is moving from laboratories to day-to-day applications. However, even measuring a user's affective response, usually only provides indications of the user's attitude to the content in its entirety, such as revealing the user's response to a whole viewed scene, or the last minute of game play.
The aforementioned methods fall short when it comes to understanding the user's attitude towards specific details, which may be valuable for effective personalization. These methods also fall short of answering simple questions such as, how does the user feel towards a specific character in a game? Which villain elicits a stronger reaction in a battle scene? Would a user prefer that a presenter in an insurance advertisement be a man or a woman? Should that presenter be dressed in casual or formal attire? Should the sofa in the background be blue or beige? Knowing such details can help make personalized content that suits a user's specific taste, which makes the content more engaging and likeable.
Thus, there is a need to be able to discern specific details regarding a user's preferences in order to make more accurate user models and improve content personalization for users.